Anterior cingulate and medial prefrontal cortex oscillations underlie learning alterations in trait anxiety in humans

Anxiety has been linked to altered belief formation and uncertainty estimation, impacting learning. Identifying the neural processes underlying these changes is important for understanding brain pathology. Here, we show that oscillatory activity in the medial prefrontal, anterior cingulate and orbitofrontal cortex (mPFC, ACC, OFC) explains anxiety-related learning alterations. In a magnetoencephalography experiment, two groups of human participants pre-screened with high and low trait anxiety (HTA, LTA: 39) performed a probabilistic reward-based learning task. HTA undermined learning through an overestimation of volatility, leading to faster belief updating, more stochastic decisions and pronounced lose-shift tendencies. On a neural level, we observed increased gamma activity in the ACC, dmPFC, and OFC during encoding of precision-weighted prediction errors in HTA, accompanied by suppressed ACC alpha/beta activity. Our findings support the association between altered learning and belief updating in anxiety and changes in gamma and alpha/beta activity in the ACC, dmPFC, and OFC.

In the same convolution model, we observed that the discrete stimulus regressors induced a pronounced drop in beta activity in HTA when compared to LTA in the right cACC (shown here for blue stimulus on the left; similar results for blue stimulus on the right, not shown). This reduction was maintained throughout the post-stimulus interval (significant clusters from 100 until 700 ms, before the feedback presentation; P = 0.001, FWER-controlled). d) Same as b) but for the stimulus (blue left) regressor. The relative reduction in beta activity shown in c) was associated with increases in the individual beta-band TF responses to the stimulus regressor in LTA participants, and reductions in HTA participants. e-f) Stimulus-locked analysis of the response regressor (left response). LTA and HTA groups exhibited similar TF images in association with the response regressor. Panels E and F illustrate the expected alpha reduction prior to and during the response, followed by a pronounced beta rebound effect. There were no between-group differences in the TF images to the discrete regressors in any of our ROIs or additional anatomical labels (P > 0.05).  Tables   Table S1   Model Prior  Table S1. Means and variances of the priors on perceptual parameters and starting values of the beliefs of the HGF models. Values are shown for 3-level HGF, 2-level HGF and HGFμ3 models. Free parameters are estimated in their unbounded space. Parameters that are restricted to a confined interval are log-transformed, to allow for estimation in an unbounded space: ζ, σ2 (0) , σ3 (0) , κ. The prior variances are given in the space in which the parameters are typically estimated. We fixed some of these parameters, however (prior variance = 0 in the space in which they are estimated). As in recent work 14,84 , the initial values of the belief trajectories were fixed in each individual for the 3-level HGF and 2-level HGF models: μ2 (0) , σ2 (0) , μ3 (0) , σ3 (0) . We estimated ω2, ω3 in each participant (ω2 only for the 2-level HGF). The winning model HGFμ3 had as free parameters ω2, ω3 , μ3 (0) , and σ3 (0) , and the mapping from beliefs to decisions was a function of 0.3/0.7, 0.1/0.9, reflecting the probability that blue/orange images are rewarding. Our task script did pseudorandomly generate the order of these phases independently in each block and participant.

Supplementary
Accordingly, it was possible that the same probability mapping would occur at the end of block 1 and, after a break, at the beginning of block 2. To exclude the possibility that group differences in the pseudorandomised order of contingency mappings could explain the behavioural and computational results, we conducted a series of validation analyses. First, we assessed whether there were between-group differences in the true experienced volatility, as three HTA participants and four LTA participants did not experience a change in contingency mapping from block 1 to block 2, which slightly decreased their overall true volatility relative to the remaining 16 HTA and 16 LTA participants.
We evaluated between-group differences in the experienced true volatility by computing Bayes This approach was implemented using measures of the true experienced volatility as DV.
BF values were interpreted as in ref. 119 . As BF is the ratio between the probability of the data being observed under the alternative hypothesis and the probability of the same data under the null hypothesis, BF10 =likelihood of data given H1/ likelihood of data given H0 a BF10 of 20 would indicate strong evidence for the alternative hypothesis. On the other hand, BF of 0.05 would provide strong evidence for the null hypothesis (see Table 1 by ref. 119 for further details).
We estimated the following quantities to reflect the true experienced volatility by each participant: (a) Number of switches in contingency mapping: this was 9 in all participants except for 3 HTA and 4 LTA participants, who observed 8 switches respectively. We found moderate evidence for the null hypothesis, supporting that this quantity is equal in both groups: BF10 = 0.21 (Bayes factor in range 1/3 -1/10 is associated with moderate evidence for H0 Second, the group-average contingency mapping is displayed in Supplementary Figure 1b. We observe that the group-average Pr(win|blue) trajectory closely overlaps in both groups throughout the task, except during trials 90-95. To assess whether the probabilistic relationships were different in each group, we conducted a second Bayes factor analysis.
We estimated BF for a factorial analysis to assess the effect of group and trial bins on Pr(win|blue).
To this aim, we used as full model DV ~ 1 + group*bin, which includes three categorical fixed effects: group, bin and interaction group:bin. Next, we constructed the restricted models by excluding each of the main or interaction effects. We then computed the ratio of the full model and each restricted model. The resulting BF provided evidence for either main effect (group, bin) or interaction effect.
Here, we transformed the 320 trials into a categorical variable: 10 bins of 32 trials. Thus, we assessed the BF of a 10 x 2 ANOVA model with factors bin (10 levels: average within each bin of 32 trials) and group (HTA, LTA). The results revealed a BF=6.7482e-04 for the main effect of bin, demonstrating extreme evidence for H0 and supporting that the average Pw(blue|win) was not modulated across bins.
The main effect of group was associated with a BF = 0.0655, providing strong evidence in favour of H0 that the population mean in both groups was equal.
Last, we obtained a BF = 0.0015 for the interaction effect. This demonstrated extreme evidence for a lack of interaction effect.
Our BF analyses above demonstrate that, although we pseudorandomised the contingency phases separately in each individual and block, and 3/19 HTA and 4/20 LTA participants did not experience a switch from block 1 to 2, this did not contribute to group differences in true experienced volatility or the stimulus-outcome probabilistic relationships.
We complemented the BF analyses with a reanalysis of the main behavioural and computational variables in the two subsamples of 16 HTA and 16 LTA participants who experienced exactly nine switches in contingency mappings, thus excluding the 3 HTA and 4 LTA participants who observed a repetition of the contingency mapping from block 1 to block 2. This analysis effectively regresses out any potential differences in the experienced true contingency. Figure 2:

Reanalysis of computational variables in
The figure above is similar to Figure 2e-h but represents the group analysis with the subsamples of 16 HTA and 16 LTA participants observing 9 switches in probabilistic mapping. The participants who observed 8 switches are marked as "excluded" and denoted by the crosses. Visual inspection of the excluded participants in panels A and B indicates that some participants had a large expectation on log-volatility despite being exposed to slightly smaller true volatility (8 switches instead of 9).
Between-group statistical analysis in the 16-16 subsamples demonstrated that: (A) HTA individuals (red) had a greater initial expectation or prior on log-volatility than LTA Using the HTA and LTA subsamples, we also replicated the result that HTA had a smaller win rate in the first block (P = 0.0370, non-parametric effect size Δ = 0.7031) but not in the second one (P = 0.9181, Δ = 0.5039), when compared to LTA.
Accordingly, when excluding participants with 8 instead of 9 contingency mapping changes, we replicate the main behavioural and computational results of the study (note however the smaller sample sizes). Crucially, HTA participants in the total sample and subsample had a greater expectation on log-volatility, primarily due to an initially higher estimate. Thus, the switch in contingencies from block 1 to block 2 has negligible effects on the inferred volatility estimate in the task. It is also important to note that HTA individuals in the total sample and subsample had a greater expectation on informational belief uncertainty (σ2). Accordingly, they update their beliefs about the tendency of the stimulus-outcome contingencies faster, using greater steps. This computational result is the main driver of the neural oscillatory effects on pwPE, which we also replicate in the subsamples: